Female Workers in Japan

• Large gap in earnings and high ratio of part-time jobs
• Female participation is not low

What Do I Do?

Data

Japan Panel Study of Employment Dynamics (JPSED)

• 57,284 men and women older than 15 in Japan
• Panel data 2015-2019
• Earnings, working hours, housework, labor contracts (main earner and spouse)
• Use samples aged 25-59

Survey on Dual-Income Couples’ Household Economy and Attitudes

• 2200 couples, aged 35-49 (30-55) for females (males), in the Greater Tokyo Area
• One-year survey in 2014
• Earnings, working hours, housework, types of contracts

Regular and Non-regular Jobs

In Japanese statistics, a definition is used: Regular and Non-regular Jobs

• Based on “how their occupations are classified in the company”
• There is no precise definition

Distribution of Weekly Working Hours

Distribution of Hourly Wage (1K JPY ≈ 6.9 EUR)

Occupational Choices of Married Men and Women

Why Do Women Choose Non-regular Jobs?

Job Flexibility and Convex Earning

Goldin (2014) defines the two types of jobs by earning schedule

• Linear jobs are lower wages and high flexibility
• Non-linear (convex) jobs are high wage and low flexibility

These characteristics correspond to Regular and Non-regular jobs!
Regression↗

Social Norms

Marriage Penalty

Key Features

1. Regular vs. Non-regular
2. Social norms on wife’s earnings

Households’ Problem

Productivity

Each husband and wife is endowed job specific productivity:

\[ \begin{pmatrix}a_{m, R} \\ a_{f, R} \\ a_{m, NR} \\ a_{f, NR}\end{pmatrix} \sim \log\mathcal{N}\left(\begin{pmatrix}0 \\ 0 \\ \mu_{NR} \\ \mu_{NR}\end{pmatrix}, \begin{pmatrix} \sigma^2 & \rho_{mf}\sigma^2 & \rho_{R, NR}\sigma^2 & \rho_{R, NR}\rho_{mf} \sigma^2 \\ \cdot & \sigma^2 & \rho_{R, NR}\rho_{mf} \sigma^2 & \rho_{R, NR} \sigma^2 \\ \cdot & \cdot & \sigma^2 & \rho_{mf} \sigma^2 \\ \cdot & \cdot & \cdot & \sigma^2 \end{pmatrix}\right) \]

Convex Wage Schedules

Leisure Function

\[ H = \left(\nu(1 - h_m - T_m)^{\xi} + (1 - \nu)(1 - h_f - T_f)^{\xi}\right)^{1/\xi} \]

Calibration Strategy

15 Parameters

\[ \{\underbrace{\lambda_{R}, \lambda_{NR},\theta, }_{\text{production function}} \underbrace{\mu_{NR}, \sigma^2, \rho_{R, NR}, \rho_{mf},}_{\text{productivity}} \,\, \underbrace{\gamma, \xi, \alpha_{\nu}, \beta_{\nu},}_{\text{leisure}} \underbrace{\alpha_{T}, \beta_{T},}_{\text{home hours }} \underbrace{\alpha_{\delta}, \beta_{\delta}}_{\text{social norm}}\} \]

Method of Simulated Moments

1. Model produces occupations, working hours, and wages of household
2. Compute 15 moments (e.g. ratio of regular workers, mean of working hours, gender correlation of wage…)
3. Minimize the distance between moments from data and model

Estimation

Estimation

Estimation

Estimation

Occupational Choices (Not-Targeted)

Time Allocations (Not-Targeted)

Social Norms

Gender Gaps

Gender Gaps

Model explains

• Almost all the gap in the participation rate

Gender Gaps

Model explains

• Almost all the gap in the participation rate
• Significant proportion of other gender gaps

Why Are the Gaps Large in Japan?

Flexible Regular Job: Occupational Choices

No Social Norm: Occupational Choices

Mechanism

Mechanism

Job inflexibility \(\theta\)

• The main element prevents women from having regular jobs
• Wage gap comes from occupational differences

Mechanism

Job inflexibility \(\theta\)

• The main element that prevents women from having regular jobs
• Wage gap comes from occupational differences

Social Norms \(\delta\)

• Explains intensive and extensive margin of male and female participation

Conclusion

Outsourcing of Housework

Outsourcing housework could increase women’s labor supply

Baseline Model with Housework Service

\[\max_{h_m, h_f, j_m, j_f} U = \log c + \gamma \log H - \delta \mathbb{1}(e_m < e_f)\]

subject to

\[ \begin{aligned} c + pt &= e(h_m, j_m) + e(h_f, j_f)\\ H &= (\nu(1 - h_m - T_m)^\xi + (1 - \nu)(1 - h_f - T_f)^\xi)^{1/\xi} \\ T &= T_m + T_f + t \end{aligned} \]

Outsourcing \(T\): Home Hours

Outsourcing \(T\): Gender Gaps

Outsourcing \(T\): Gender Gaps

Given social norms, housework services

• Reduce gender gaps in participation, occ. choices, and labor hours

Outsourcing \(T\): Gender Gaps

Given social norms, housework services

• Reduce gender gaps in participation, occupational choices, and labor hours
• Do not reduce wage gap

Slide↩︎

Related Literature

Home Hours and Gender Gaps

Erosa et al. (2022)

• Models couples’ decisions on occupations with different job flexibility

Cubas, Juhn, and Silos (2019)

• Women are penalized for the lack of work in the peak hours (8am-5pm)

Social Norms and Occupational Choices

Bertrand, Kamenica, and Pan (2015)

• A sharp gap in the wife’s earnings relative to the husband’s earnings

Gender Gaps in Japan

Kitao and Mikoshiba (2022)

• Role of fiscal policies on female labor force participation and occ. choices

Job Flexibility and Convex Earning

To see the convex and linear wage schedules, similar to
Bick, Blandin, and Rogerson (2022),

\[y_{it} = a_{i} + \lambda_t + \left(\sum_{h \in H, h \ne 40} \beta_h I_{ith}\right) + \gamma X_{it} + \varepsilon_{it}\]

Earning Curves

Slide↩︎

Marriage Penalty

Slide↩︎